This invention relates to processes for making fibers that are both extruded and oriented, which processes utilize multivariate data analysis techniques.
Many processes for making extruded, oriented fibers are known. Such fibers are extruded from molten polymer and then drawn by various methods in order to orient the fiber. Orientation is a known term of art and generally means that the molecules of the fiber are aligned in order to give the extruded fiber certain physical properties required for its particular use. Such fibers are used in a number of applications. For example, extruded and oriented fibers are used in optical fibers and for the manufacture of optical wave guides. In addition, such fibers may be used in the textile industry, e.g. in the manufacture of clothing, carpets, etc. Such fibers also may be used in the manufacture of surgical sutures.
Due to the complex relationship of the various process and product variables in such processes and the extremely short time in which extrusion and orientation occurs, the manufacture of such fibers requires critical control and coordination of the extrusion and orientation processes. In addition, conventional start-up of such conventional processes often requires numerous iterations of monitoring, testing, adjusting, etc. and often results in lost time and raw material due to excessive iterations.
It would be advantageous to provide processes for making extruded, oriented fibers, which processes provide improved control over product and process variables.
The present invention includes processes for making fibers. The processes comprise an extrusion step wherein molten polymers are extruded through an extrusion die, thereby forming at least one extruded filament; and an orientation step, wherein the filament is drawn to align the molecules of the filament, and wherein multiple process variables of the extrusion step, and optionally the orientation step, are monitored and data collected with respect thereto. The collected data are analyzed using a multivariate data analysis technique and, optionally, process variables are adjusted in response to the multivariate data analysis.